{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 课程1.4---Numpy随机数\n",
    "## numpy.random包含多种概率分布的随机样本，是数据分析辅助的重点工具之一"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-1.05018108e+00  8.78928063e-01  8.08034135e-01 -2.73124244e-01]\n",
      " [-7.07294427e-02  2.22841402e+00  1.57981130e-01 -1.12925873e+00]\n",
      " [ 4.45193034e-01 -2.68302795e-01  1.12938796e-03 -1.22127540e+00]\n",
      " [-4.29703249e-01 -7.73718923e-01  7.52022720e-01  1.61750437e+00]]\n"
     ]
    }
   ],
   "source": [
    "# 随机数生成\n",
    "\n",
    "import numpy as np\n",
    "samples = np.random.normal(size = (4,4))\n",
    "print(samples)\n",
    "# 生成一个标准正态分布的4*4的样本值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.11538761056657554 <class 'float'>\n",
      "[0.40566962 0.80889876 0.55259582 0.3698483 ] <class 'numpy.ndarray'>\n",
      "[[0.31454415 0.71294873 0.49942191]\n",
      " [0.09504013 0.8189146  0.74923354]] <class 'numpy.ndarray'>\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1cb214021d0>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# numpy.random.rand(d0, d1, ..., dn)：生成一个[0,1)之间的随机浮点数或N维浮点数组 —— 均匀分布\n",
    "\n",
    "import matplotlib.pyplot as plt  # 导入matplotlib模块，用于图表辅助分析\n",
    "% matplotlib inline \n",
    "# 魔法函数，每次运行自动生成图表\n",
    "\n",
    "a = np.random.rand()\n",
    "print(a,type(a))  # 生成一个随机浮点数\n",
    "\n",
    "b = np.random.rand(4)\n",
    "print(b,type(b))  # 生成形状为4的一维数组\n",
    "\n",
    "c = np.random.rand(2,3)\n",
    "print(c,type(c))  # 生成形状为2*3的二维数组，注意这里不是((2,3))\n",
    "\n",
    "samples1 = np.random.rand(1000)\n",
    "samples2 = np.random.rand(1000)\n",
    "plt.scatter(samples1,samples2)\n",
    "# 生成1000个均匀分布的样本值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1cb2149b5f8>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#  numpy.random.randn(d0, d1, ..., dn)：生成一个浮点数或N维浮点数组 —— 正态分布\n",
    "\n",
    "samples1 = np.random.randn(1000)\n",
    "samples2 = np.random.randn(1000)\n",
    "plt.scatter(samples1,samples2)\n",
    "# randn和rand的参数用法一样\n",
    "# 生成1000个正太的样本值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "[0 0 0 1 1]\n",
      "[3 5 2 5 3]\n",
      "[[0 1 0]\n",
      " [0 0 1]]\n",
      "[[5 3 4]\n",
      " [5 3 3]]\n"
     ]
    }
   ],
   "source": [
    "# numpy.random.randint(low, high=None, size=None, dtype='l')：生成一个整数或N维整数数组\n",
    "# 若high不为None时，取[low,high)之间随机整数，否则取值[0,low)之间随机整数，且high必须大于low \n",
    "# dtype参数：只能是int类型  \n",
    "\n",
    "print(np.random.randint(2))\n",
    "# low=2：生成1个[0,2)之间随机整数  \n",
    "\n",
    "print(np.random.randint(2,size=5))\n",
    "# low=2,size=5 ：生成5个[0,2)之间随机整数\n",
    "\n",
    "print(np.random.randint(2,6,size=5))\n",
    "# low=2,high=6,size=5：生成5个[2,6)之间随机整数  \n",
    "\n",
    "print(np.random.randint(2,size=(2,3)))\n",
    "# low=2,size=(2,3)：生成一个2x3整数数组,取数范围：[0,2)随机整数 \n",
    "\n",
    "print(np.random.randint(2,6,(2,3)))\n",
    "# low=2,high=6,size=(2,3)：生成一个2*3整数数组,取值范围：[2,6)随机整数  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
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